Copyright © Allyn & Bacon 2007 Chapter 2 Research Methods This multimedia product and its contents are protected under copyright law. The following are prohibited by law: Any public performance or display, including transmission of any image over a network; Preparation of any derivative work, including the extraction, in whole or in part, of any images Any rental, lease or lending of the program. ISBN:

Copyright © Allyn & Bacon 2007 How Do Psychologists Develop New Knowledge? Psychologists, like researchers in all other sciences, use the scientific method to test their ideas empirically

Copyright © Allyn & Bacon 2007 Empirical investigation – An approach to research that relies on sensory experience and observation as research data How Do Psychologists Develop New Knowledge? Scientific method – A five-step process for empirical investigation of a hypothesis under conditions designed to control biases and subjective judgments

Copyright © Allyn & Bacon 2007 The Five Steps of the Scientific Method Developing a hypothesis Performing a controlled test Gathering objective data Analyzing the results Publishing, criticizing, and replicating the results

Copyright © Allyn & Bacon 2007 Developing a hypothesis Performing a controlled test Gathering objective data Analyzing the results Publishing, criticizing, and replicating the results Hypothesis – A statement predicting the outcome of a scientific study Operational definitions – Exact procedures used in establishing experimental conditions and measurement of results The Five Steps of the Scientific Method Sample – Identifies who will be included in the experiment. Representative Sample – A representation of the population at large.

Copyright © Allyn & Bacon 2007 Developing a hypothesis Performing a controlled test Gathering objective data Analyzing the results Publishing, criticizing, and replicating the results Independent variable (I.V.) The variable manipulated by the experimenter Random Assignment –Using chance alone to determine which group the subjects are placed in. The Five Steps of the Scientific Method Experimental Group – This is the group of subjects who get the I.V., or the variable that is being manipulated or tested. Control Group (placebo group)– The group that is used as a “control” or as a comparison to make sure that the I.V. is responsible for the results and not just chance.

Copyright © Allyn & Bacon 2007 Developing a hypothesis Performing a controlled test Gathering objective data Analyzing the results Publishing, criticizing, and replicating the results Data – Information gathered by researcher and used to test a hypothesis Dependent variable (D.V.) – The measured outcome of a study; the responses of participants in a study The Five Steps of the Scientific Method

Copyright © Allyn & Bacon 2007 Developing a hypothesis Performing a controlled test Gathering objective data Analyzing the results Publishing, criticizing, and replicating the results Based on statistical analyses of results, the hypothesis is accepted or rejected The Five Steps of the Scientific Method In experiments, the researcher controls all the conditions and directly manipulates the conditions Confounding or Extraneous Variables – Those “extra” variables that can “confound” or interfere with the results.

Copyright © Allyn & Bacon 2007 Developing a hypothesis Performing a controlled test Gathering objective data Analyzing the results Publishing, criticizing, and replicating the results The Five Steps of the Scientific Method Researchers must find out whether their work can withstand the scrutiny of the scientific community

Copyright © Allyn & Bacon 2007 Types of Psychological Research Non-experimental methods include: Correlational studies Surveys Naturalistic observation Longitudinal studies Cross-sectional studies Cohort-sequential studies Ex-Post Facto Design

Copyright © Allyn & Bacon 2007 Correlations: A relationship between Two Variables Correlation– A relationship between two variables, in which changes in one variable are reflected in changes in the other variable Correlation coefficient– A number between -1.0 and +1.0 expressing the degree of relationship between two variables

Copyright © Allyn & Bacon 2007 A + correlation can be just as strong as a – correlation (-.92 is exactly as strong a correlation as +.92) Correlation does not imply causation! Positive Correlation: as one variable changes, the other variable changes in the same direction. The more you study, the higher your test scores are. Negative Correlation: as one variable changes, the other variable changes in the opposite direction. The more time you spend on FB, the lower your test scores are. No correlation: There is no relationship between the variables. No relationship between shoe size and intelligence!

Surveys A METHOD OF RESEARCH THAT INVOLVES ASKING SUBJECTS (PEOPLE) QUESTIONS ABOUT THEIR FEELINGS, OPINIONS, OR BEHAVIOR PATTERNS. THESE CAN BE “YES/NO” QUESTIONS THESE CAN BE FILL IN THE BLANK THESE CAN BE “SCALE” QUESTIONS ELICITS “QUICK” INFORMATION, BUT NOT A RELIABLE MEANS OF RESEARCH. Copyright © Allyn & Bacon 2007

Naturalistic Observation A RESEARCH METHOD THAT TAKES PLACE IN THE SUBJECT’S NATURAL ENVIRONMENT. THE SUBJECT IS UNAWARE THEY ARE BEING OBSERVED. Copyright © Allyn & Bacon 2007

Longitudinal Study Copyright © Allyn & Bacon 2007 A RESEARCH METHOD THAT STUDIES OR FOLLOWS AND OBSERVES THE SAME GROUP OF PEOPLE OVER A LONG PERIOD OF TIME. GENERALLY, THIS IS LONGER THAN 10 YEARS.

Cross-Sectional Studies A METHOD THAT LOOKS AT DIFFERENT AGE GROUPS AT THE SAME TIME IN ORDER TO UNDERSTAND CHANGES THAT OCCUR DURING THE LIFE SPAN. AN EXAMPLE WOULD BE QUESTIONING DIFFERENT AGE GROUPS AND HOW THEY FEEL ABOUT TOPICS LIKE WAR, POLITICS, RELIGION. Copyright © Allyn & Bacon 2007

Cohort –Sequential Studies THIS IS LIKE A COMBINATION OF THE CROSS-SECTIONAL AND THE LONGITUDINAL METHODS. TAKES A CROSS-SECTION OF THE POPULATION, AND THEN FOLLOWS THAT COHORT (GROUP) FOR A PERIOD OF TIME. YIELDS BETTER DATA THAN THE CROSS- SECTIONAL METHOD. Copyright © Allyn & Bacon 2007

Ex-Post Facto Design PRE-EXISITING FACTORS. SUBJECTS ARE CHOSEN BASED ON A PRE-EXISTING CONDITION. USEFUL TO HELP DISCOVER TREATMENT METHODS FOR ILLNESS, OR MENTAL ILLNESS Copyright © Allyn & Bacon 2007

Sources of Bias Sources of bias include: Personal bias Expectancy bias Bias could affect the way an experimenter designs a study, collects data, or interprets results Researchers must also attempt to control confounding variables

Double Blind vs. Single Blind Studies Single Blind The subjects do not know which group they belong to (either experimental or control group). The researchers know who is in which group. Can lead to experimenter bias. Double Blind The subjects do not know which group they belong to (either experimental or control group). The researchers also do not know who is in which group. Very beneficial in studies where new drugs are being tested. Copyright © Allyn & Bacon 2007

Ethics in Research Deception Debriefing Animal research See Table 2.3 on page 37 in your book Copyright © Allyn & Bacon 2007

Questions Science Cannot Answer The scientific method is not appropriate for answering questions that cannot be put to an objective, empirical test Ethics Morality Religious beliefs Preferences

Copyright © Allyn & Bacon 2007 How Do We Make Sense of the Data? Researchers use statistics for two major purposes: (1) descriptively to characterize measurements made on groups or individuals and (2) inferentially to judge whether these measurements are the result of chance

Copyright © Allyn & Bacon 2007 Organizing the Data First results must be arranged in a summary chart known as a frequency distribution We can convert the data into a bar graph called a histogram

Copyright © Allyn & Bacon 2007 Frequency distribution chart – shows how frequently each score occurred. Histogram or bar chart – gives a visual representation of how the scores look. This helps us to “see” whether or not the scores are evenly distributed. A histogram can also show whether or not the scores are more clustered around the middle of the distribution or if there are outliers (extreme scores). Examples of organizing the data:

Descriptive Statistics In order to understand the data that was gathered, statistics help to bring the data into sharper focus. When using statistics, researchers are looking for the central point around which the numbers seem to cluster. This is called “measures of central tendency.” This will then help the researchers to make inferences about the data to determine if the results are reliable or simply due to chance (e.g. inferential statistics). Copyright © Allyn & Bacon 2007

Describing the Data With Descriptive Statistics Descriptive statistics: Numbers that describe the main characteristics of the data. The mean The median The mode The range The standard deviation The normal distribution

The Mean The measure of central tendency most often used to describe a set of data. Add all the scores and divide by the number of scores. It is the average. While it is a pretty good indicator of the center of the distribution, its one flaw is that it can be skewed by extreme scores. So, if the distribution of the scores is relatively symmetrical (bell shaped), there is no problem; however, if more scores fall toward either end of the distribution, then the mean gets pulled in that direction and distorts the overall inference of the data. Copyright © Allyn & Bacon 2007

Examples of Skewed Distributions Copyright © Allyn & Bacon 2007 On the last test, the class mean was 68. But, because it was not a symmetrical distribution, that sounds like the class overall did poorly. When calculating the median the scores look much better: the median score was 72. Due to low extreme scores, the mean is a not a very good indicator of how the class did. More low scores than high scores – but there are a few extremely high scores (mean is higher than the median) More high scores than low – but there are a few extremely low scores (mean is lower than the median)

The Median The “middle” score. Think of the “median divider” in the center of the road – it divides the upper half of the scores from the lower half. This is a better measure of central tendency because it is not affected by extreme scores. The scores are listed in order, and it is the number in the middle. (e.g., 50, 55, 60, 65, 70) If you have an uneven set of numbers, take the two middle numbers, add them, and divide by 2. (e.g., 50, 55, 60, 65, 70, 72..add 60+65/2=62.5). Copyright © Allyn & Bacon 2007

The Mode A measure of central tendency that is used to identify the score that occurs the mode, ooops, the most! 55, 55, 55, 63, 68, 70, 70, 82, 95 It is often the least useful measure of central tendency, especially if the sample is small. Copyright © Allyn & Bacon 2007

The Range The simplest measure of central tendency that represents the difference between the highest and the lowest values. You use the range all the time in school when you see what differentiates an A from a B ( and 80-89). Copyright © Allyn & Bacon 2007

The Standard Deviation Psychologists prefer to take all scores into consideration, not just the highest and the lowest, so they use the standard deviation instead. The SD is a measure of central tendency that shows an average difference between each score and the mean. So, we are looking at the changes in the scores across the spectrum of the scores. The larger the SD, the more spread out the scores are; the smaller the SD, the more the scores bunch together at the mean. Copyright © Allyn & Bacon 2007

The Normal Distribution Together, the SD and the mean tell us much about a distribution of scores. They indicate where the center of the distribution is and how closely the scores cluster around the center. In a normal distribution, or a bell curve, the scores are all equally distributed around the mean. Copyright © Allyn & Bacon 2007

Normal Distribution Copyright © Allyn & Bacon % of values % of scores 68% of scores fall within 1 SD above and below the mean If you have 100 scores, 50 are above, and 50 are below Know how to compute percentile Know how to compute Z score 2%14%34% 14%2% 95% of values 99% of values Mean Percentiles 0 Standard Deviations from the Mean Z Scores nd16th50th th 98th 100th

Copyright © Allyn & Bacon 2007 Making Inferences with Inferential Statistics Inferential statistics are used to assess whether the results of a study are reliable or whether they might be simply the result of chance. Researchers use inferential statistics to determine whether or not the findings can be applied to the larger population from which the sample was selected. Researchers compare the results of the experimental group to the control group and determine (infer) whether the differences between the groups are a result of the Independent Variable or could be the result of chance. To have confidence in the results, the researchers have to take into account the magnitude of the differences in scores, and go back to make sure the sample was large enough and that the sample was representative of the population at large. While a sample can never truly represent the entire population, researchers do look at sampling error, or how chance plays a factor in the results.

Making Inferences with Inferential Statistics Researchers then compute a “p” value for the scores, which states how probable the results are due to the IV or chance. What you need to know is that, in psychology, the cutoff for statistical significance, or that the results are probably due to the IV, is a value of p<.05. This means that the probability of the results of the experiment being due to chance are less than 5%, or 5 in 100. A “p” value can never equal zero because we can never be 100% sure that results did not happen due to chance. Therefore, researchers often try to replicate their results to gather more evidence that their initial findings were not due to chance. Copyright © Allyn & Bacon 2007

End of Chapter 2